




'''simple docstring'''



import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase_							:	List[Any]	     =		logging.getLogger(__name__)





@dataclass
class      lowerCAmelCase :
       __lowercase	:   str										=      field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
       __lowercase	:   Optional[str]										=      field(
           default='''NER'''					, metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
       __lowercase	:   bool										=      field(default=__lowerCAmelCase					, metadata={'''help''': '''Set this flag to use fast tokenization.'''})
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					, )





@dataclass
class      lowerCAmelCase :
       __lowercase	:   str										=      field(
           metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}					, )
       __lowercase	:   int										=      field(
           default=128					, metadata={
               '''help''': (
                   '''The maximum total input sequence length after tokenization. Sequences longer '''
                   '''than this will be truncated, sequences shorter will be padded.'''
               )
           }					, )
       __lowercase	:   bool										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     if len(sys.argv						) == 2 and sys.argv[1].endswith('''.json'''						):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          __snake_case       ,     __snake_case       ,     __snake_case            =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __snake_case       ,     __snake_case       ,     __snake_case            =	parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir						)
         and os.listdir(training_args.output_dir						)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
              ''' --overwrite_output_dir to overcome.'''						)

     __snake_case            =	import_module('''tasks'''						)
     try:
          __snake_case            =	getattr(_lowerCamelCase ,						model_args.task_type						)
          __snake_case            =	token_classification_task_clazz()
     except AttributeError:
          raise ValueError(
              f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
              f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''						)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,						datefmt='''%m/%d/%Y %H:%M:%S''' ,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,						)
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,						training_args.local_rank ,						training_args.device ,						training_args.n_gpu ,						bool(training_args.local_rank != -1						) ,						training_args.fpaa ,						)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank						):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('''Training/evaluation parameters %s''' ,						_lowerCamelCase						)

     # Set seed
     set_seed(training_args.seed						)

     # Prepare CONLL-2003 task
     __snake_case            =	token_classification_task.get_labels(data_args.labels						)
     __snake_case            =	dict(enumerate(_lowerCamelCase						)						)
     __snake_case            =	len(_lowerCamelCase						)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     __snake_case            =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path ,						num_labels=_lowerCamelCase ,						idalabel=_lowerCamelCase ,						labelaid={label: i for i, label in enumerate(_lowerCamelCase						)} ,						cache_dir=model_args.cache_dir ,						)
     __snake_case            =	AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,						cache_dir=model_args.cache_dir ,						use_fast=model_args.use_fast ,						)
     __snake_case            =	AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path						) ,						config=_lowerCamelCase ,						cache_dir=model_args.cache_dir ,						)

     # Get datasets
     __snake_case            =	(
         TokenClassificationDataset(
             token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.train ,						)
         if training_args.do_train
         else None
     )
     __snake_case            =	(
         TokenClassificationDataset(
             token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.dev ,						)
         if training_args.do_eval
         else None
     )

     def align_predictions(_lowerCamelCase				:  np.ndarray ,						_lowerCamelCase				:  np.ndarray						) -> Tuple[List[int], List[int]]:
          __snake_case            =	np.argmax(_lowerCamelCase ,						axis=2						)

          __snake_case       ,     __snake_case            =	preds.shape

          __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
          __snake_case            =	[[] for _ in range(_lowerCamelCase						)]

          for i in range(_lowerCamelCase						):
               for j in range(_lowerCamelCase						):
                    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                         out_label_list[i].append(label_map[label_ids[i][j]]						)
                         preds_list[i].append(label_map[preds[i][j]]						)

          return preds_list, out_label_list

     def compute_metrics(_lowerCamelCase				:  EvalPrediction						) -> Dict:
          __snake_case       ,     __snake_case            =	align_predictions(p.predictions ,						p.label_ids						)
          return {
              "accuracy_score": accuracy_score(_lowerCamelCase ,						_lowerCamelCase						),
              "precision": precision_score(_lowerCamelCase ,						_lowerCamelCase						),
              "recall": recall_score(_lowerCamelCase ,						_lowerCamelCase						),
              "f1": fa_score(_lowerCamelCase ,						_lowerCamelCase						),
          }

     # Data collator
     __snake_case            =	DataCollatorWithPadding(_lowerCamelCase ,						pad_to_multiple_of=8						) if training_args.fpaa else None

     # Initialize our Trainer
     __snake_case            =	Trainer(
         model=_lowerCamelCase ,						args=_lowerCamelCase ,						train_dataset=_lowerCamelCase ,						eval_dataset=_lowerCamelCase ,						compute_metrics=_lowerCamelCase ,						data_collator=_lowerCamelCase ,						)

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_process_zero():
               tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
     __snake_case            =	{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''						)

          __snake_case            =	trainer.evaluate()

          __snake_case            =	os.path.join(training_args.output_dir ,						'''eval_results.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    logger.info('''***** Eval results *****'''						)
                    for key, value in result.items():
                         logger.info('''  %s = %s''' ,						_lowerCamelCase ,						_lowerCamelCase						)
                         writer.write('''%s = %s\n''' % (key, value)						)

               results.update(_lowerCamelCase						)

    # Predict
     if training_args.do_predict:
          __snake_case            =	TokenClassificationDataset(
              token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.test ,						)

          __snake_case       ,     __snake_case       ,     __snake_case            =	trainer.predict(_lowerCamelCase						)
          __snake_case       ,     __snake_case            =	align_predictions(_lowerCamelCase ,						_lowerCamelCase						)

          __snake_case            =	os.path.join(training_args.output_dir ,						'''test_results.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    for key, value in metrics.items():
                         logger.info('''  %s = %s''' ,						_lowerCamelCase ,						_lowerCamelCase						)
                         writer.write('''%s = %s\n''' % (key, value)						)

        # Save predictions
          __snake_case            =	os.path.join(training_args.output_dir ,						'''test_predictions.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    with open(os.path.join(data_args.data_dir ,						'''test.txt'''						) ,						'''r'''						) as f:
                         token_classification_task.write_predictions_to_file(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)

     return results







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple						)-> int:




     '''simple docstring'''





     main()


if __name__ == "__main__":
     main()











'''simple docstring'''



def        UpperCAmelCase (    A	:					int					):
							if not isinstance(A       ,		A					):
														SCREAMING_SNAKE_CASE       :   List[str]         =      F"""Input value of [number={number}] must be an integer"""
														raise TypeError(A					)
							if number < 0:
														return False
							SCREAMING_SNAKE_CASE       :   List[str]         =      number * number
							while number > 0:
														if number % 10 != number_square % 10:
																					return False
														number //= 10
														number_square //= 10
							return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()




